Predictive Modelling for Employee Retention

Abstract

Organizations face enormous issues when it comes to employee turnover, which is why they need to develop accurate predictive models for retention. The purpose of this chapter is to present a three-tiered machine learning approach for predicting employee turnover that makes use of resume parsing, performance analysis, and advanced algorithms. In addition, the authors make use of Intel oneAPI, which is a unified programming model that is increasingly becoming the industry standard, in order to improve the scalability and performance of the solution. The system that is offered delivers full HR (human resource) analytics, which enables firms to make educated decisions regarding recruiting and retention tactics. The results of the experimental evaluation show that the solution is effective in providing an accurate forecast of attrition, which paves the way for proactive retention measures. The approach enhances system performance by utilizing oneAPI, which in turn ensures that it is scalable over a variety of different hardware architectures.